Physiological Parameter Monitoring Using Pre-Interaction Baseline Analysis
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Solution Overview
Problem
In clinical settings, monitoring physiological parameters like respiration rate and blood pressure can be affected by the patient's awareness of being monitored, leading to inaccurate readings, as these parameters change due to interaction with clinicians or environmental factors.
Innovation Solution
The system determines characteristic physiological parameters by analyzing data from a predefined time period before a triggering event, using methods such as average, median, or weighted averages, and confidence measures to isolate true values unaffected by patient interaction, employing continuous wavelet transforms to process signals from pulse oximetry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physiological parameters are monitored continuously in clinical settings, then diagnostic information is obtained, but the patient's awareness of being monitored alters the parameters leading to inaccurate readings
Solution Approach 1:
The system performs preliminary analysis of physiological parameter data before the triggering event (patient interaction) occurs. By identifying and analyzing the pre-interaction baseline period, the system establishes the true characteristic values of physiological parameters before they are altered by patient awareness or interaction effects.
Solution Approach 2:
The monitoring data is segmented into different time periods: pre-interaction baseline period and post-interaction period. The system separately analyzes the pre-interaction segment to determine characteristic values, effectively isolating the harmful interaction effect from the measurement accuracy.
2Measurement precision
If characteristic physiological parameters are determined by analyzing pre-triggering event data, then accurate true values are obtained, but additional processing time and complexity are required
Solution Approach 1:
The system automatically identifies triggering events and autonomously selects the appropriate pre-triggering time window for analysis without requiring manual intervention. The processor self-manages the complex task of identifying baseline periods, applying statistical methods, and determining characteristic values, reducing operational complexity despite the sophisticated processing required.
Solution Approach 2:
The system employs multiple statistical parameter methods (average, median, mode, weighted average) to analyze the pre-triggering data. By changing the mathematical parameters used in analysis, the system can optimize accuracy while managing computational complexity through selective application of appropriate statistical methods.
Data Source
AI summary
The present disclosure relates to monitoring a characteristic physiological parameter of a patient during a suitable time period that either precedes or follows a triggering event, such as a clinician/patient interaction, that may negatively impact the physiological parameter. In some embodiments, physiological parameter values falling between one or more pre-set thresholds may be used to derive the characteristic physiological parameter. In some embodiments, tracking the physiological parameter may provide additional information about the patient's status. In some embodiments, confidence measures may be associated with, or may be used to analyze features of the patient signal to derive information about, the characteristic physiological parameter. The patient signal used to derive a patient's physiological parameter may be of an oscillatory nature or may include oscillatory features that may be analyzed to derive a characteristic blood pressure or a characteristic respiration rate.